Evidence map›Paper›PMID 42900349›Full record

ArticleDrug safety2026

Strengthening Signal Detection by Proactive Adverse Event Case Identification in Electronic Health Records Using a Text-Mining Tool.

Gabriëlla von Kreijfelt, Juliëtte Zwaveling, Jette A van Lint, Loes E Visser, Agnes C Kant

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Article in Drug safety, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Gabriëlla von KreijfeltThe Netherlands Pharmacovigilance Centre Lareb, 's-Hertogenbosch, The Netherlands. g.j.von_kreijfelt@lumc.nl.ORCID http://orcid.org/0009-0009-6521-9592
Juliëtte ZwavelingDepartment of Clinical Pharmacology and Toxicology, Leiden University Medical Centre, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.
Jette A van LintThe Netherlands Pharmacovigilance Centre Lareb, 's-Hertogenbosch, The Netherlands.
Loes E VisserDepartment of Pharmacy, Haga Teaching Hospital, The Hague, The Netherlands.
Agnes C KantThe Netherlands Pharmacovigilance Centre Lareb, 's-Hertogenbosch, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe spontaneous reporting system is the cornerstone of pharmacovigilance but is limited by substantial underreporting, which may delay safety signal detection.

objectiveThe aim of this study was to evaluate the electronic health record-adverse event search method for identifying and reporting additional potential adverse drug reactions in electronic health records to strengthen routine signal detection.

methodsBetween August 2022 and August 2025, targeted searches for adverse drug reactions were conducted in approximately 2,750,000 electronic health records from two Dutch hospitals using a data collection text-mining tool. Search criteria were derived from information in spontaneous reports of specific drug-adverse event associations. Structured (e.g. laboratory results, medication prescriptions) and unstructured (e.g. clinical notes, hospital discharge letters) data were used to identify potential adverse drug reactions. Identified cases were manually verified and, when relevant, reported to the Netherlands Pharmacovigilance Centre Lareb and analysed alongside spontaneous reports for signal detection.

resultsA total of 147 electronic health record-derived cases were reported for 41 drug-adverse event associations. These contributed to 34 signal detection analyses, supporting the assessment of seven safety signals. In 75% of the cases, a suspected causal relationship was documented by healthcare professionals in clinical notes. Electronic health record-derived cases accounted for up to 73% of the reports included in the seven detected signals.

conclusionsThis study demonstrates that electronic health records can be used as an additional source of individual case safety reports in routine pharmacovigilance practice, supporting the further assessment of selected potential safety signals. Despite limitations related to efficiency, this proactive approach has potential to address underreporting and complement the spontaneous reporting system, strengthening signal detection.

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